Instructions to use edugp/data2vec-nlp-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edugp/data2vec-nlp-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="edugp/data2vec-nlp-base")# Load model directly from transformers import AutoModelForMaskedLM model = AutoModelForMaskedLM.from_pretrained("edugp/data2vec-nlp-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| tags: | |
| model-index: | |
| - name: data2vec-nlp-base | |
| results: [] | |
| # Data2Vec NLP Base | |
| This model was converted from `fairseq`. | |
| The original weights can be found in https://dl.fbaipublicfiles.com/fairseq/data2vec/nlp_base.pt | |
| Example usage: | |
| ```python | |
| from transformers import RobertaTokenizer, Data2VecForSequenceClassification, Data2VecConfig | |
| import torch | |
| tokenizer = RobertaTokenizer.from_pretrained("roberta-large") | |
| config = Data2VecConfig.from_pretrained("edugp/data2vec-nlp-base") | |
| model = Data2VecForSequenceClassification.from_pretrained("edugp/data2vec-nlp-base", config=config) | |
| # Fine-tune this model | |
| inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") | |
| outputs = model(**inputs) | |
| prediction_logits = outputs.logits | |
| ``` | |